-
Notifications
You must be signed in to change notification settings - Fork 1
/
info.json
20 lines (20 loc) · 1.39 KB
/
info.json
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
{
"abstract": "In this article, we dwell into the class of so-called ill-posed Linear Inverse Problems (LIP) which simply refer to the task of recovering the entire signal from its relatively few random linear measurements. Such problems arise in a variety of settings with applications ranging from medical image processing, recommender systems, etc. We propose a slightly generalized version of the error constrained linear inverse problem and obtain a novel and equivalent convex-concave min-max reformulation by providing an exposition to its convex geometry. Saddle points of the min-max problem are completely characterized in terms of a solution to the LIP, and vice versa. Applying simple saddle point seeking ascend-descent type algorithms to solve the min-max problems provides novel and simple algorithms to find a solution to the LIP. Moreover, the reformulation of an LIP as the min-max problem provided in this article is crucial in developing methods to solve the dictionary learning problem with almost sure recovery constraints.",
"authors": [
"Mohammed Rayyan Sheriff",
"Debasish Chatterjee"
],
"emails": [
"mohammedrayyan@sc.iitb.ac.in",
"dchatter@iitb.ac.in"
],
"id": "20-707",
"issue": 28,
"pages": [
1,
46
],
"title": "Novel Min-Max Reformulations of Linear Inverse Problems",
"volume": 23,
"year": 2022
}